Development of a technique to determine the 3D elasticity tensor of wood as applied to drying stress modeling
Bibliographic record
Abstract
The objective of this study was to develop an accurate and simple method for measuring the engineering coefficients of the 3D elasticity tensor of wood. A method using a semi-ring extensometer (SRE) and a compression specimen (6-specimen technique) is proposed. The SRE is made of a semi-ring stainless steel blade pin-jointed to two aluminum fixing plates, and two resistance strain gauges bonded to the top and bottom faces of the blade at mid-span position. Groups of five matched compression specimens (20 mm x 20 mm x 60 mm) from black spruce wood (Picea mariana (Mill.) B.S.P.), cut in six different orientations with respect to load axis (three orthotropic directions and three diagonal directions at an angle of 45 degrees to the load axis) were used for the calibration of the SRE. A resistance strain gauge bonded directly to the wood surface was used as a reference for both the axial and transverse measurements. The validation of the technique was made with another series of specimens cut in the same six orientations. The axial strain data of the SRE were then compared to the ones obtained from a linear displacement sensor (LVDT). For the transverse directions, the SRE results in terms of Poisson’s ratios and shear moduli were compared with corresponding data obtained from the literature. Results showed that the R2 value of the relationship between the Young’s moduli determined with the SRE and the LVDT varied from 0.88 to 0.97. The SRE technique appeared also reliable to evaluate both the Poisson’s ratios and shear moduli as the obtained values were in good agreement with the literature data. As compared to bonded strain gauges, the SRE technique is reusable, simpler and cheaper to use and its sensitivity is nearly independent of temperature.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".